Covid-19 Visualization¶

Import Package¶

In [12]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from datascience import *
import warnings 
warnings.filterwarnings("ignore")

Import Dataset¶

In [2]:
confirmed = pd.read_csv('time_series_covid19_confirmed_global_narrow.csv', skiprows = [1], dtype = {'Value': int}) #remove the 2nd row

confirmed['Status'] = 'Confirmed'

confirmed
Out[2]:
Province/State Country/Region Lat Long Date Value ISO 3166-1 Alpha 3-Codes Region Code Sub-region Code Intermediate Region Code Status
0 NaN Afghanistan 33.939110 67.709953 2022-02-02 164190 AFG 142.0 34.0 NaN Confirmed
1 NaN Afghanistan 33.939110 67.709953 2022-02-01 163555 AFG 142.0 34.0 NaN Confirmed
2 NaN Afghanistan 33.939110 67.709953 2022-01-31 162926 AFG 142.0 34.0 NaN Confirmed
3 NaN Afghanistan 33.939110 67.709953 2022-01-30 162111 AFG 142.0 34.0 NaN Confirmed
4 NaN Afghanistan 33.939110 67.709953 2022-01-29 161290 AFG 142.0 34.0 NaN Confirmed
... ... ... ... ... ... ... ... ... ... ... ...
208035 NaN Zimbabwe -19.015438 29.154857 2020-01-26 0 ZWE 2.0 202.0 14.0 Confirmed
208036 NaN Zimbabwe -19.015438 29.154857 2020-01-25 0 ZWE 2.0 202.0 14.0 Confirmed
208037 NaN Zimbabwe -19.015438 29.154857 2020-01-24 0 ZWE 2.0 202.0 14.0 Confirmed
208038 NaN Zimbabwe -19.015438 29.154857 2020-01-23 0 ZWE 2.0 202.0 14.0 Confirmed
208039 NaN Zimbabwe -19.015438 29.154857 2020-01-22 0 ZWE 2.0 202.0 14.0 Confirmed

208040 rows × 11 columns

In [3]:
death = pd.read_csv('time_series_covid19_deaths_global_narrow.csv', skiprows = [1], dtype = {'Value': int})

death['Status'] = 'Death'

death
Out[3]:
Province/State Country/Region Lat Long Date Value ISO 3166-1 Alpha 3-Codes Region Code Sub-region Code Intermediate Region Code Status
0 NaN Afghanistan 33.939110 67.709953 2022-02-02 7418 AFG 142.0 34.0 NaN Death
1 NaN Afghanistan 33.939110 67.709953 2022-02-01 7417 AFG 142.0 34.0 NaN Death
2 NaN Afghanistan 33.939110 67.709953 2022-01-31 7414 AFG 142.0 34.0 NaN Death
3 NaN Afghanistan 33.939110 67.709953 2022-01-30 7408 AFG 142.0 34.0 NaN Death
4 NaN Afghanistan 33.939110 67.709953 2022-01-29 7405 AFG 142.0 34.0 NaN Death
... ... ... ... ... ... ... ... ... ... ... ...
208035 NaN Zimbabwe -19.015438 29.154857 2020-01-26 0 ZWE 2.0 202.0 14.0 Death
208036 NaN Zimbabwe -19.015438 29.154857 2020-01-25 0 ZWE 2.0 202.0 14.0 Death
208037 NaN Zimbabwe -19.015438 29.154857 2020-01-24 0 ZWE 2.0 202.0 14.0 Death
208038 NaN Zimbabwe -19.015438 29.154857 2020-01-23 0 ZWE 2.0 202.0 14.0 Death
208039 NaN Zimbabwe -19.015438 29.154857 2020-01-22 0 ZWE 2.0 202.0 14.0 Death

208040 rows × 11 columns

Merge Dataset¶

In [4]:
total = pd.concat([confirmed, death], ignore_index = True)

total
Out[4]:
Province/State Country/Region Lat Long Date Value ISO 3166-1 Alpha 3-Codes Region Code Sub-region Code Intermediate Region Code Status
0 NaN Afghanistan 33.939110 67.709953 2022-02-02 164190 AFG 142.0 34.0 NaN Confirmed
1 NaN Afghanistan 33.939110 67.709953 2022-02-01 163555 AFG 142.0 34.0 NaN Confirmed
2 NaN Afghanistan 33.939110 67.709953 2022-01-31 162926 AFG 142.0 34.0 NaN Confirmed
3 NaN Afghanistan 33.939110 67.709953 2022-01-30 162111 AFG 142.0 34.0 NaN Confirmed
4 NaN Afghanistan 33.939110 67.709953 2022-01-29 161290 AFG 142.0 34.0 NaN Confirmed
... ... ... ... ... ... ... ... ... ... ... ...
416075 NaN Zimbabwe -19.015438 29.154857 2020-01-26 0 ZWE 2.0 202.0 14.0 Death
416076 NaN Zimbabwe -19.015438 29.154857 2020-01-25 0 ZWE 2.0 202.0 14.0 Death
416077 NaN Zimbabwe -19.015438 29.154857 2020-01-24 0 ZWE 2.0 202.0 14.0 Death
416078 NaN Zimbabwe -19.015438 29.154857 2020-01-23 0 ZWE 2.0 202.0 14.0 Death
416079 NaN Zimbabwe -19.015438 29.154857 2020-01-22 0 ZWE 2.0 202.0 14.0 Death

416080 rows × 11 columns

Total Covid-19 Cases All Over the World Until 2/2/2022¶

In [5]:
tot_num = total.query("Date == '2022-02-02'")[["Status", "Value"]].groupby(["Status"], as_index = False).sum()

tot_num
Out[5]:
Status Value
0 Confirmed 384859233
1 Death 5700923
In [13]:
fig, ax = plt.subplots(figsize = (12, 8))  # using this when plt.figure(figsize = (12, 8)) doesn't work
tot_num.plot(kind = "barh", x = "Status", legend = False, ax = ax)
plt.title("Total Confirmed Cases vs. Death Cases Worldwide", y = 1.05, x = 0.5)
plt.xlabel("")
plt.ylabel("Status")
plt.ticklabel_format(axis = "x", style = "plain") # plt.ticklabel_format(axis="y", style="sci", scilimits=(0,0))
plt.show()

Total Confirmed & Death Cases for Each Country/Region¶

In [9]:
df = total.query("Date == '2022-02-02'")[["Date", "Country/Region", "Status", "Value"]].groupby(["Status", "Country/Region"], as_index = False).sum()

df
Out[9]:
Status Country/Region Value
0 Confirmed Afghanistan 164190
1 Confirmed Albania 261240
2 Confirmed Algeria 254885
3 Confirmed Andorra 36315
4 Confirmed Angola 98267
... ... ... ...
387 Death Vietnam 37777
388 Death West Bank and Gaza 5116
389 Death Yemen 2018
390 Death Zambia 3921
391 Death Zimbabwe 5352

392 rows × 3 columns

Top 10 Countries/Regions with Most Confirmed Cases¶

In [10]:
con_top = df.query("Status == 'Confirmed'").sort_values(by = ['Value'], ascending = False).head(10)

con_top
Out[10]:
Status Country/Region Value
182 Confirmed US 75680487
79 Confirmed India 41803318
23 Confirmed Brazil 25820745
62 Confirmed France 19998301
186 Confirmed United Kingdom 17632062
144 Confirmed Russia 11936064
181 Confirmed Turkey 11833165
85 Confirmed Italy 11235745
66 Confirmed Germany 10474992
164 Confirmed Spain 10125348
In [14]:
sns.catplot(x = "Value", y = "Country/Region", data = con_top, kind = "bar", height = 8, aspect = 12/8, palette = "RdYlBu")
plt.title("Top 10 Countries with Most Confirmed Cases", y = 1.05, x = 0.5)
plt.xlabel("Cases")
plt.ticklabel_format(axis = "x", style = "plain") 
plt.show()

Top 10 Countries/Regions with Most Death Cases¶

In [15]:
dea_top = df.query("Status == 'Death'").sort_values(by = ['Value'], ascending = False).head(10)

dea_top
Out[15]:
Status Country/Region Value
378 Death US 894316
219 Death Brazil 629301
275 Death India 498983
340 Death Russia 325986
311 Death Mexico 306920
334 Death Peru 206220
382 Death United Kingdom 157938
281 Death Italy 147320
276 Death Indonesia 144320
233 Death Colombia 134781
In [16]:
sns.catplot(x = "Value", y = "Country/Region", data = dea_top, kind = "bar", height = 8, aspect = 12/8, palette = "PRGn")
plt.title("Top 10 Countries with Most Death Cases", y = 1.05, x = 0.5)
plt.xlabel("Cases")
plt.ticklabel_format(axis = "x", style = "plain") 
plt.show()
In [17]:
#df.groupby('Status', as_index = False).apply(lambda x: x.sort_values('Value', ascending = False).head(10))

#df.sort_values('Value', ascending = False).groupby("Status", as_index = False).head(10).reset_index()

List of Top 10 Countries/Regions with Most Confirmed & Death Cases¶

In [18]:
nam = df.sort_values('Value', ascending = False).groupby("Status", as_index = False).head(10)["Country/Region"].values

nam
Out[18]:
array(['US', 'India', 'Brazil', 'France', 'United Kingdom', 'Russia',
       'Turkey', 'Italy', 'Germany', 'Spain', 'US', 'Brazil', 'India',
       'Russia', 'Mexico', 'Peru', 'United Kingdom', 'Italy', 'Indonesia',
       'Colombia'], dtype=object)
In [19]:
df_top = df[df["Country/Region"].isin(nam)].sort_values('Value', ascending = False)

df_top
Out[19]:
Status Country/Region Value
182 Confirmed US 75680487
79 Confirmed India 41803318
23 Confirmed Brazil 25820745
62 Confirmed France 19998301
186 Confirmed United Kingdom 17632062
144 Confirmed Russia 11936064
181 Confirmed Turkey 11833165
85 Confirmed Italy 11235745
66 Confirmed Germany 10474992
164 Confirmed Spain 10125348
37 Confirmed Colombia 5916825
115 Confirmed Mexico 4985689
80 Confirmed Indonesia 4353370
138 Confirmed Peru 3286151
378 Death US 894316
219 Death Brazil 629301
275 Death India 498983
340 Death Russia 325986
311 Death Mexico 306920
334 Death Peru 206220
382 Death United Kingdom 157938
281 Death Italy 147320
276 Death Indonesia 144320
233 Death Colombia 134781
258 Death France 132583
262 Death Germany 118339
360 Death Spain 93857
377 Death Turkey 87831

Convert Long Table into Wide Table¶

In [20]:
pd.pivot(df_top, index = "Country/Region", columns = "Status", values = "Value").rename_axis(columns = None).reset_index()
Out[20]:
Country/Region Confirmed Death
0 Brazil 25820745 629301
1 Colombia 5916825 134781
2 France 19998301 132583
3 Germany 10474992 118339
4 India 41803318 498983
5 Indonesia 4353370 144320
6 Italy 11235745 147320
7 Mexico 4985689 306920
8 Peru 3286151 206220
9 Russia 11936064 325986
10 Spain 10125348 93857
11 Turkey 11833165 87831
12 US 75680487 894316
13 United Kingdom 17632062 157938

Convert a Pandas DataFrame into a Table¶

In [21]:
new = Table.from_df(pd.pivot(df_top, index = "Country/Region", columns = "Status", values = "Value").rename_axis(columns = None).reset_index())

new
Out[21]:
Country/Region Confirmed Death
Brazil 25820745 629301
Colombia 5916825 134781
France 19998301 132583
Germany 10474992 118339
India 41803318 498983
Indonesia 4353370 144320
Italy 11235745 147320
Mexico 4985689 306920
Peru 3286151 206220
Russia 11936064 325986

... (4 rows omitted)

In [22]:
new.sort("Confirmed", descending = True).barh("Country/Region", height = 8, width = 12)
plt.title("Confirmed Cases vs. Death Cases", y = 1.05, x = 0.5)
plt.xlabel("Cases")
plt.ticklabel_format(axis = "x", style = "plain") # plt.ticklabel_format(axis="y", style="sci", scilimits=(0,0))    
plt.show()

Visualization on Map¶

In [23]:
loc = total.drop_duplicates(subset = "Country/Region")[["Country/Region", "Lat", "Long"]]

loc
Out[23]:
Country/Region Lat Long
0 Afghanistan 33.939110 67.709953
743 Albania 41.153300 20.168300
1486 Algeria 28.033900 1.659600
2229 Andorra 42.506300 1.521800
2972 Angola -11.202700 17.873900
... ... ... ...
204325 Vietnam 14.058324 108.277199
205068 West Bank and Gaza 31.952200 35.233200
205811 Yemen 15.552727 48.516388
206554 Zambia -13.133897 27.849332
207297 Zimbabwe -19.015438 29.154857

196 rows × 3 columns

In [24]:
newloc = pd.merge(df, loc, how = 'inner', on = ['Country/Region'])

newloc
Out[24]:
Status Country/Region Value Lat Long
0 Confirmed Afghanistan 164190 33.939110 67.709953
1 Death Afghanistan 7418 33.939110 67.709953
2 Confirmed Albania 261240 41.153300 20.168300
3 Death Albania 3362 41.153300 20.168300
4 Confirmed Algeria 254885 28.033900 1.659600
... ... ... ... ... ...
387 Death Yemen 2018 15.552727 48.516388
388 Confirmed Zambia 305959 -13.133897 27.849332
389 Death Zambia 3921 -13.133897 27.849332
390 Confirmed Zimbabwe 230012 -19.015438 29.154857
391 Death Zimbabwe 5352 -19.015438 29.154857

392 rows × 5 columns

In [25]:
import geopandas
cov19data = geopandas.GeoDataFrame(newloc, geometry = geopandas.points_from_xy(newloc.Long, newloc.Lat))
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world_cov19 = geopandas.sjoin(world, cov19data, how = "inner", op = 'intersects')

world_cov19
Out[25]:
pop_est continent name iso_a3 gdp_md_est geometry index_right Status Country/Region Value Lat Long
0 920938 Oceania Fiji FJI 8374.0 MULTIPOLYGON (((180.00000 -16.06713, 180.00000... 121 Death Fiji 809 -17.713400 178.065000
0 920938 Oceania Fiji FJI 8374.0 MULTIPOLYGON (((180.00000 -16.06713, 180.00000... 120 Confirmed Fiji 63147 -17.713400 178.065000
1 53950935 Africa Tanzania TZA 150600.0 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... 348 Confirmed Tanzania 33230 -6.369028 34.888822
1 53950935 Africa Tanzania TZA 150600.0 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... 349 Death Tanzania 789 -6.369028 34.888822
3 35623680 North America Canada CAN 1674000.0 MULTIPOLYGON (((-122.84000 49.00000, -122.9742... 64 Confirmed Canada 3081616 53.933300 -116.576500
... ... ... ... ... ... ... ... ... ... ... ... ...
174 1895250 Europe Kosovo -99 18490.0 POLYGON ((20.59025 41.85541, 20.52295 42.21787... 186 Confirmed Kosovo 210561 42.602636 20.902977
175 1218208 North America Trinidad and Tobago TTO 43570.0 POLYGON ((-61.68000 10.76000, -61.10500 10.890... 359 Death Trinidad and Tobago 3423 10.691800 -61.222500
175 1218208 North America Trinidad and Tobago TTO 43570.0 POLYGON ((-61.68000 10.76000, -61.10500 10.890... 358 Confirmed Trinidad and Tobago 112679 10.691800 -61.222500
176 13026129 Africa S. Sudan SSD 20880.0 POLYGON ((30.83385 3.50917, 29.95350 4.17370, ... 326 Confirmed South Sudan 16810 6.877000 31.307000
176 13026129 Africa S. Sudan SSD 20880.0 POLYGON ((30.83385 3.50917, 29.95350 4.17370, ... 327 Death South Sudan 137 6.877000 31.307000

342 rows × 12 columns

Covid-19 Confirmed Cases¶

In [26]:
fig, ax = plt.subplots(1, 1, figsize = (15,10))
world_cov19.query("Status == 'Confirmed'").plot(column = 'Value', ax = ax, cmap = 'Reds', scheme = 'NaturalBreaks', k = 6, legend = True,
                legend_kwds = {'loc':'lower left','title':'Covid-19 Confirmed Cases'})
plt.show()
In [27]:
fig, ax = plt.subplots(1, 1, figsize = (15,10))

world_cov19.query("Status == 'Confirmed'").plot(column = 'Value', cmap = 'OrRd', scheme = 'quantiles', ax = ax, legend = True,
                                               legend_kwds = {'loc':'lower left','title':'Covid-19 Confirmed Cases'})

plt.show()

Covid-19 Death Cases¶

In [28]:
fig, ax = plt.subplots(1, 1, figsize = (15,10))
world_cov19.query("Status == 'Death'").plot(column = 'Value', ax = ax, cmap = 'Blues', scheme = 'NaturalBreaks', k = 6, legend = True,
                legend_kwds = {'loc':'lower left','title':'Covid-19 Death Cases'})
plt.show()
In [29]:
fig, ax = plt.subplots(1, 1, figsize = (15,10))
world_cov19.query("Status == 'Death'").plot(column = 'Value', ax = ax, legend = True)
plt.show()

Confirmed & Death Cases Every Day¶

In [30]:
dtotal = total[["Date", "Country/Region", "Status", "Value"]].groupby(["Date","Status"], as_index = False).sum()

dtotal
Out[30]:
Date Status Value
0 2020-01-22 Confirmed 557
1 2020-01-22 Death 17
2 2020-01-23 Confirmed 655
3 2020-01-23 Death 18
4 2020-01-24 Confirmed 941
... ... ... ...
1481 2022-01-31 Death 5674374
1482 2022-02-01 Confirmed 381683860
1483 2022-02-01 Death 5688629
1484 2022-02-02 Confirmed 384859233
1485 2022-02-02 Death 5700923

1486 rows × 3 columns

In [31]:
import plotly.express as px

# color pallette
cnf, dth = '#2131bf', '#ff2e63'

fig = px.area(dtotal, x = "Date", y = "Value", color = 'Status', height = 600,
             title = 'Cases Over Time', color_discrete_sequence = [cnf,dth])
fig.show()
In [ ]: